{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Implementing a Neural Network\n",
    "In this exercise we will develop a neural network with fully-connected layers to perform classification, and test it out on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting future\n",
      "Installing collected packages: future\n",
      "Successfully installed future-0.17.1\n"
     ]
    }
   ],
   "source": [
    "!pip install future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# A bit of setup\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cs231n.classifiers.neural_net import TwoLayerNet\n",
    "\n",
    "from __future__ import print_function\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the class `TwoLayerNet` in the file `cs231n/classifiers/neural_net.py` to represent instances of our network. The network parameters are stored in the instance variable `self.params` where keys are string parameter names and values are numpy arrays. Below, we initialize toy data and a toy model that we will use to develop your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a small net and some toy data to check your implementations.\n",
    "# Note that we set the random seed for repeatable experiments.\n",
    "\n",
    "input_size = 4\n",
    "hidden_size = 10\n",
    "num_classes = 3\n",
    "num_inputs = 5\n",
    "\n",
    "def init_toy_model():\n",
    "    np.random.seed(0)\n",
    "    return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)\n",
    "\n",
    "def init_toy_data():\n",
    "    np.random.seed(1)\n",
    "    X = 10 * np.random.randn(num_inputs, input_size)\n",
    "    y = np.array([0, 1, 2, 2, 1])\n",
    "    return X, y\n",
    "\n",
    "net = init_toy_model()\n",
    "X, y = init_toy_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute scores\n",
    "Open the file `cs231n/classifiers/neural_net.py` and look at the method `TwoLayerNet.loss`. This function is very similar to the loss functions you have written for the SVM and Softmax exercises: It takes the data and weights and computes the class scores, the loss, and the gradients on the parameters. \n",
    "\n",
    "Implement the first part of the forward pass which uses the weights and biases to compute the scores for all inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "correct scores:\n",
      "[[-0.81233741 -1.27654624 -0.70335995]\n",
      " [-0.17129677 -1.18803311 -0.47310444]\n",
      " [-0.51590475 -1.01354314 -0.8504215 ]\n",
      " [-0.15419291 -0.48629638 -0.52901952]\n",
      " [-0.00618733 -0.12435261 -0.15226949]]\n",
      "\n",
      "Difference between your scores and correct scores:\n",
      "3.680272093239262e-08\n"
     ]
    }
   ],
   "source": [
    "scores = net.loss(X)\n",
    "print('Your scores:')\n",
    "print(scores)\n",
    "print()\n",
    "print('correct scores:')\n",
    "correct_scores = np.asarray([\n",
    "  [-0.81233741, -1.27654624, -0.70335995],\n",
    "  [-0.17129677, -1.18803311, -0.47310444],\n",
    "  [-0.51590475, -1.01354314, -0.8504215 ],\n",
    "  [-0.15419291, -0.48629638, -0.52901952],\n",
    "  [-0.00618733, -0.12435261, -0.15226949]])\n",
    "print(correct_scores)\n",
    "print()\n",
    "\n",
    "# The difference should be very small. We get < 1e-7\n",
    "print('Difference between your scores and correct scores:')\n",
    "print(np.sum(np.abs(scores - correct_scores)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Forward pass: compute loss\n",
    "In the same function, implement the second part that computes the data and regularizaion loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference between your loss and correct loss:\n",
      "1.7985612998927536e-13\n"
     ]
    }
   ],
   "source": [
    "loss, _ = net.loss(X, y, reg=0.05)\n",
    "correct_loss = 1.30378789133\n",
    "\n",
    "# should be very small, we get < 1e-12\n",
    "print('Difference between your loss and correct loss:')\n",
    "print(np.sum(np.abs(loss - correct_loss)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backward pass\n",
    "Implement the rest of the function. This will compute the gradient of the loss with respect to the variables `W1`, `b1`, `W2`, and `b2`. Now that you (hopefully!) have a correctly implemented forward pass, you can debug your backward pass using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W2 max relative error: 3.440708e-09\n",
      "b2 max relative error: 4.447646e-11\n",
      "W1 max relative error: 3.561318e-09\n",
      "b1 max relative error: 2.738421e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.gradient_check import eval_numerical_gradient\n",
    "\n",
    "# Use numeric gradient checking to check your implementation of the backward pass.\n",
    "# If your implementation is correct, the difference between the numeric and\n",
    "# analytic gradients should be less than 1e-8 for each of W1, W2, b1, and b2.\n",
    "\n",
    "loss, grads = net.loss(X, y, reg=0.05)\n",
    "\n",
    "# these should all be less than 1e-8 or so\n",
    "for param_name in grads:\n",
    "    f = lambda W: net.loss(X, y, reg=0.05)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, net.params[param_name], verbose=False)\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the network\n",
    "To train the network we will use stochastic gradient descent (SGD), similar to the SVM and Softmax classifiers. Look at the function `TwoLayerNet.train` and fill in the missing sections to implement the training procedure. This should be very similar to the training procedure you used for the SVM and Softmax classifiers. You will also have to implement `TwoLayerNet.predict`, as the training process periodically performs prediction to keep track of accuracy over time while the network trains.\n",
    "\n",
    "Once you have implemented the method, run the code below to train a two-layer network on toy data. You should achieve a training loss less than 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final training loss:  0.017149607938732093\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "net = init_toy_model()\n",
    "stats = net.train(X, y, X, y,\n",
    "            learning_rate=1e-1, reg=5e-6,\n",
    "            num_iters=100, verbose=False)\n",
    "\n",
    "print('Final training loss: ', stats['loss_history'][-1])\n",
    "\n",
    "# plot the loss history\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('training loss')\n",
    "plt.title('Training Loss history')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the data\n",
    "Now that you have implemented a two-layer network that passes gradient checks and works on toy data, it's time to load up our favorite CIFAR-10 data so we can use it to train a classifier on a real dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 3072)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 3072)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "from cs231n.data_utils import load_CIFAR10\n",
    "\n",
    "def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=1000):\n",
    "    \"\"\"\n",
    "    Load the CIFAR-10 dataset from disk and perform preprocessing to prepare\n",
    "    it for the two-layer neural net classifier. These are the same steps as\n",
    "    we used for the SVM, but condensed to a single function.  \n",
    "    \"\"\"\n",
    "    # Load the raw CIFAR-10 data\n",
    "    cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "    X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "        \n",
    "    # Subsample the data\n",
    "    mask = list(range(num_training, num_training + num_validation))\n",
    "    X_val = X_train[mask]\n",
    "    y_val = y_train[mask]\n",
    "    mask = list(range(num_training))\n",
    "    X_train = X_train[mask]\n",
    "    y_train = y_train[mask]\n",
    "    mask = list(range(num_test))\n",
    "    X_test = X_test[mask]\n",
    "    y_test = y_test[mask]\n",
    "\n",
    "    # Normalize the data: subtract the mean image\n",
    "    mean_image = np.mean(X_train, axis=0)\n",
    "    X_train -= mean_image\n",
    "    X_val -= mean_image\n",
    "    X_test -= mean_image\n",
    "\n",
    "    # Reshape data to rows\n",
    "    X_train = X_train.reshape(num_training, -1)\n",
    "    X_val = X_val.reshape(num_validation, -1)\n",
    "    X_test = X_test.reshape(num_test, -1)\n",
    "\n",
    "    return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "\n",
    "\n",
    "# Invoke the above function to get our data.\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a network\n",
    "To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1000: loss 2.302954\n",
      "iteration 100 / 1000: loss 2.302550\n",
      "iteration 200 / 1000: loss 2.297648\n",
      "iteration 300 / 1000: loss 2.259602\n",
      "iteration 400 / 1000: loss 2.204170\n",
      "iteration 500 / 1000: loss 2.118565\n",
      "iteration 600 / 1000: loss 2.051535\n",
      "iteration 700 / 1000: loss 1.988466\n",
      "iteration 800 / 1000: loss 2.006591\n",
      "iteration 900 / 1000: loss 1.951473\n",
      "Validation accuracy:  0.287\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "net = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "\n",
    "# Train the network\n",
    "stats = net.train(X_train, y_train, X_val, y_val,\n",
    "            num_iters=1000, batch_size=200,\n",
    "            learning_rate=1e-4, learning_rate_decay=0.95,\n",
    "            reg=0.25, verbose=True)\n",
    "\n",
    "# Predict on the validation set\n",
    "val_acc = (net.predict(X_val) == y_val).mean()\n",
    "print('Validation accuracy: ', val_acc)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss function and train / validation accuracies\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(stats['loss_history'])\n",
    "plt.title('Loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(stats['train_acc_history'], label='train')\n",
    "plt.plot(stats['val_acc_history'], label='val')\n",
    "plt.title('Classification accuracy history')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Clasification accuracy')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can, with a fully-connected Neural Network. For every 1% above 52% on the Test set we will award you with one extra bonus point. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "params: learning_rate:0.0015, reg:0.03, batch_size:200, hidden_size:500\n",
      "y_train_acc: 0.5801632653061225, y_val_acc: 0.505\n"
     ]
    }
   ],
   "source": [
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_net.                                                            #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on the previous exercises.                          #\n",
    "#################################################################################\n",
    "\n",
    "# try indepedently\n",
    "learning_rates = [1.5e-3]  # [0.5e-3, 1e-3, 1.5e-3, 2e-3, 3e-3]\n",
    "regularization_strengths = [0.03]  # [0.03, 0.05, 0.1, 0.15, 0.2]\n",
    "hidden_sizes = [500]  # [200, 300, 400, 500, 600, 700]\n",
    "batch_sizes = [200]\n",
    "\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_net = None  # store the best model into this \n",
    "\n",
    "for learning_rate in learning_rates:\n",
    "  for reg in regularization_strengths:\n",
    "    for batch_size in batch_sizes:\n",
    "      for hidden_size in hidden_sizes:\n",
    "        print('params: learning_rate:%s, reg:%s, batch_size:%s, hidden_size:%s' %\n",
    "              (learning_rate, reg, batch_size, hidden_size))\n",
    "        net = TwoLayerNet(input_size=32*32*3, hidden_size=hidden_size, output_size=10)\n",
    "        stats = net.train(X_train, y_train, X_val, y_val,\n",
    "                  num_iters=1500, batch_size=batch_size,\n",
    "                  learning_rate=learning_rate, learning_rate_decay=0.95,\n",
    "                  reg=reg, verbose=False)\n",
    "        y_train_pred = net.predict(X_train)\n",
    "        y_train_acc = np.mean(y_train == y_train_pred)\n",
    "        y_val_pred = net.predict(X_val)\n",
    "        y_val_acc = np.mean(y_val == y_val_pred)\n",
    "        print('y_train_acc: %s, y_val_acc: %s' %(y_train_acc, y_val_acc))\n",
    "\n",
    "        if y_val_acc > best_val:\n",
    "          best_val = y_val_acc\n",
    "          best_net = net\n",
    "#################################################################################\n",
    "#                               END OF YOUR CODE                                #\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# visualize the weights of the best network\n",
    "show_net_weights(best_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run on the test set\n",
    "When you are done experimenting, you should evaluate your final trained network on the test set; you should get above 48%.\n",
    "\n",
    "**We will give you extra bonus point for every 1% of accuracy above 52%.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:  0.521\n"
     ]
    }
   ],
   "source": [
    "test_acc = (best_net.predict(X_test) == y_test).mean()\n",
    "print('Test accuracy: ', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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